Typically, every process generation of semiconductor technology development involves a shrink in the physical dimensions of the manufacturing process and a redesign of the base architecture of the transistor. At the onset of a new technology, electrical and physical constraints are determined, to which the entire manufacturing process conforms.
A primary challenge within each technology is to ascertain the optimal transistor architecture within the ranges of the electrical and physical constraints that produces the best combination of performance and manufacturability. In addition to shrinking the transistors (replacing a 0.15-micron process with a 0.10-micron process, for example), improvements in transistor switching performance, improved drive current, and reduced leakage current are among the goals. Although minor revisions to the process occur as well, major revisions typically feature performance improvements of up to 30% or more.
Prototype transistor architectures are manufactured in lots across varying geometries and processing values. A lot may include twenty-five wafers, manufactured according to different process variations, or recipes. Some wafers are processed at higher energy, some at higher temperature, and so on. Thousands of parameters, both physical and electrical, are collected for each wafer. By varying the recipes for manufacturing the wafers, the data obtained may be analyzed to determine which process variation results in the best overall transistor performance.
A critical path in transistor optimization is the data analysis time. In the early phases of technology development, the data collected is prone to large variations, both within the wafer and across other wafers in the lot. For example, data measurements for the capacitance, the drive current, or the leakage current of various circuits may be obtained. The significant variations in data make it difficult to effectively analyze the large volume of data.
During transistor optimization, there exist several challenges in analyzing the data. First, it is difficult to efficiently extract, filter, and align the large volume of data, which includes both electrical and physical characteristics. For example, one analysis includes 250 sets of die-level parameters across a run of twenty-five wafers (one lot), resulting in over 100,000 data points. The data is extracted manually, such as by using a search query language, then aligned manually using interactive statistical software. In another analysis, custom scripting automates the data extraction process.
Second, the data analysis involves the plotting of key parameters. The typical process generation includes forty graphs or plots per wafer, for a total of 1000 plots per run. Interactive statistical software may be used, along with manual interaction, to generate operating graphs one-by-one. In some cases, custom scripting may help to automate the process of creating the graphs. A highly trained engineer visually inspects each graph and manually correlates key parameters between graphs, a time-consuming procedure.
Data analysis also involves filtering and applying statistical algorithms to the collected data. Statistical techniques, such as regression analysis, are used to evaluate the relationship between various parameters, producing representative equations of the parameter relationships. There may be 1000 equations per run, as one example. Interactive statistical software, along with manual interaction, may be used to apply filtering and regression algorithms to each graph.
Using judgment obtained through experience, the highly trained engineer next adjusts the graphs, such as to remove noisy data. The engineer may also adjust an intercept point on a graph, if needed. Further, the engineer must identify all other graphs which may be affected by the adjustment (such as graphs plotting an affected parameter), and manually modify each of the dependent graphs as well. This expert-driven and time-consuming aspect of the analysis traditionally impedes automation.
The expert engineer also identifies data collection problems. A good engineer is quickly able to identify data extraction errors, for example, such as data that results from a bad probe of the wafer. A typical run of 100,000 data points may include 500 data collection problems, as one example.
The data analysis next includes determining parametric values at the boundaries of the range of the key operating constraints. For example, the process may be constrained to produce transistors whose threshold voltages are within a preset range. The analysis determines the parametric values that correspond to the maximum and minimum allowable threshold voltages. There may be a thousand parametric values per run, as one example. Typically, this step is performed using interactive statistical software, with manual intervention, to overlay boundaries of operating constraints and determine interception with data equations. Intercepts are calculated on a plot-by-plot basis, since the intercepts of one plot are used to determine the intercepts on the next plot.
Finally, the data analysis of any process generation depends on keeping up with an ever-changing transistor analysis methodology. Typically, methodologies change three to four times each year. Without being able to change parametric models and key parameters efficiently, the analysis quickly becomes stale. Currently, data analysis employs manual data collection and correlation (such as using a structured query language) and interactive statistical software. When a change in the parametric model occurs, the current data analysis paradigm is not capable of adapting automatically.
Data analysis of a process generation may be scripted to extract the data and produce the graphs. The graphs are then printed, and one or more engineers visually review each graph. Points of intercept between the constraints and the performance curved are typically determined during this manual visual inspection. Statistical algorithms that lack the capability to adjust with engineering judgment are not typically used for such data analysis because of the variation in the data obtained during technology development.
The above data analysis takes a highly trained engineer approximately one week, by one estimate, to complete the analysis. The process does not easily adapt to changes in data or in the desired analysis methodology. Further, the process is not automatic, as it includes a significant amount of manual interaction with the software programs.
Thus, there is a continuing need to develop a system for performing data analysis of a process generation that overcomes the shortcomings of the prior art.